A Novel Non-Iterative Deep Convolutional Neural Network with Kernelized Classification for Robust Face Recognition

Deep Convolutional Neural Networks (DCNNs) are very useful for image-based pattern classification problems because of their efficient feature extraction capabilities. Although DCNNs have good generalization performance, their applicability is limited due to slow learning speed, as they are based on iterative weight-update algorithms. This study presents a new noniterative DCNN that can be trained in real-time. The fundamental block of the proposed DCNN is fixed real number-based filters for convolution operations for multi-feature extraction. After a finite number of feature extraction layers, nonlinear kernel mapping along with pseudo-inverse is used for the classification of extracted feature vectors. The proposed DCNN, named Deep Convolutional Kernelized Classification (DCKC), is noniterative, as the mask coefficients of its convolution operations are fixed real numbers. The kernel function with predefined parameters of DCKC does a nonlinear mapping of extracted features, and pseudo-inverse is used to find its output weights. The proposed noniterative DCKC was evaluated on benchmark face recognition databases, achieving better results and establishing its superiority.